Original Article

Genes and Immunity (2009) 10, 446–456; doi:10.1038/gene.2009.38; published online 14 May 2009

Identification of new SLE-associated genes with a two-step Bayesian study design

D L Armstrong1,2, A Reiff1,3, B L Myones4, F P Quismorio Jr1, M Klein-Gitelman5, D McCurdy6, L Wagner-Weiner7, E Silverman8, J O Ojwang9, K M Kaufman9, J A Kelly9, J T Merrill9, J B Harley9, S-C Bae10, T J Vyse11, G S Gilkeson12, P M Gaffney9, K L Moser9, C Putterman13, J C Edberg14, E E Brown14, J Ziegler15, C D Langefeld15, R Zidovetzki2 and C O Jacob1

  1. 1The Lupus Genetic Group, Department of Medicine, University of Southern California, Los Angeles, CA, USA
  2. 2Department of Cell Biology and Neuroscience, University of California, Riverside, CA, USA
  3. 3Childrens Hospital of Los Angeles, Los Angeles, CA, USA
  4. 4Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA
  5. 5Children's Memorial Hospital and Northwestern University, Chicago, IL, USA
  6. 6Department of Pediatrics, UCLA, Los Angeles, CA, USA
  7. 7LaRabida Hospital and University of Chicago, Chicago, IL, USA
  8. 8Hospital for Sick Children, Toronto, Ontario, Canada
  9. 9Oklahoma Medical Research Foundation, Oklahoma City, OK, USA
  10. 10Department of Rheumatology, the Hospital for Rheumatic Diseases, Hanyang University, Seoul, Republic of Korea
  11. 11Imperial College London, Hammersmith Hospital, London, UK
  12. 12Medical University of South Carolina, Charleston, SC, USA
  13. 13Division of Rheumatology, Albert Einstein College of Medicine, Bronx, NY, USA
  14. 14Division of Clinical Immunology and Rheumatology, Department of Medicine, University of Alabama, Birmingham, AL, USA
  15. 15Wake Forest University Health Sciences, Winston-Salem, NC, USA

Correspondence: Professor CO Jacob, Department of Medicine, University of Southern California, 2011 Zonal Ave, HMR 705, Los Angeles, CA, 90033, USA. E-mail: jacob@usc.edu; Professor R Zidovetzki, Department of Cell Biology and Neuroscience, University of California, Riverside, CA 92525, USA. E-mail: zidovet@mail.ucr.edu

Received 28 January 2009; Accepted 26 February 2009; Published online 14 May 2009.



In our earlier study, we utilized a Bayesian design to probe the association of ~1000 genes (~10000 single-nucleotide polymorphisms (SNPs)) with systemic lupus erythematosus (SLE) on a moderate number of trios of parents and children with SLE. Two genes associated with SLE, with a multitest-corrected false discovery rate (FDR) of <0.05, were identified, and a number of noteworthy genes with FDR of <0.8 were also found, pointing out a future direction for the study. In this report, using a large population of controls and adult- or childhood-onset SLE cases, we have extended the earlier investigation to explore the SLE association of 10 of these noteworthy genes (109 SNPs). We have found that seven of these genes exhibit a significant (FDR<0.05) association with SLE, both confirming some genes that have earlier been found to be associated with SLE (PTPN22 and IRF5) and presenting novel findings of genes (KLRG1, interleukin-16, protein tyrosine phosphatase receptor type T, toll-like receptor (TLR)8 and CASP10), which have not been reported earlier. The results signify that the two-step candidate pathway design is an efficient way to study the genetic foundations of complex diseases. Furthermore, the novel genes identified in this study point to new directions in both the diagnosis and the eventual treatment of this debilitating disease.


autoimmune disease, genetic association, KLRG1, IL-16, PTPRT, TLR8



In the past 3 years, genome-wide association (GWA) studies have become extremely popular because they permit the interrogation of the entire human genome, both at levels of resolution earlier unattainable and in thousands of unrelated individuals, while remaining unconstrained by prior hypotheses regarding genetic association with the disease. Although an alternative to GWA studies, pedigree-based linkage analysis, has found disease susceptibility variants, these variants tend to have large relative risks. Furthermore, they have little effect on disease risk at a population level due to their rarity. This argument suggests that more common genetic variants, despite having more moderate relative risk, may be far more important in terms of public health simply because they are more common. GWA studies rely, therefore, on the ‘common disease, common variant’ hypothesis, which suggests that the influences of genetics on many common diseases will be at least partially attributable to a limited number of allelic variants present in more than 1–5% of the population.1, 2 But, there also exist examples of rare variants influencing common disease.3, 4 If multiple rare genetic variants were the primary cause of common complex diseases, GWA studies would have little power to detect them, particularly if allelic heterogeneity existed. Ironically, given the recent huge financial and scientific investment in GWA, there is not a great deal of evidence in support of the common disease, common variant hypothesis.5

Furthermore, the GWA approach is also problematic because the massive number of statistical tests performed presents an unprecedented potential for false-positive results, leading to multiple test correction to properly control levels of statistical significance, coupled with the increased need for replication of findings.6 If performed appropriately, correction for multiple testing will render most of the findings insignificant because of the large number of tests (greater than or equal to300000, typically).

Given that the case–control samples for GWA usually number in thousands, it might be expected that such studies are well powered. However, several authors have shown that, given the strict genome-wide significance criteria that studies must fulfill, the power of such studies is much less than might be naively imagined.7, 8

There is also a limit to how large population-based studies can get due to constraints such as budget, time and the physical number of cases in the population; so there may be a further class of variants that are too rare to be captured by GWA, but are not sufficiently at high risk to be captured by population-based linkage (for example, Cambien and Tiret9). Alternative approaches are needed to find these variants.

To counteract these shortcomings of GWA, we have adopted a Bayesian approach that concentrates on a collection of candidate pathways rather than concentrating on specific candidate genes (or the whole genome). Using these pathways, we have taken advantage of the accumulated data from pre-existing association studies of adult systemic lupus erythematosus (SLE) families, candidate gene investigations, information gained from genetics of mouse models of lupus and the gene expression profiling data of human SLE to identify sets of genes and regions containing genes that have a higher prior likelihood of association with SLE.

To implement this approach, we have developed a set of programs that embody a combination of automated and manual approaches to maximize the power of gene-association studies using prior information to select and prioritize genes, both to decrease the size of the problem and to increase the likelihood of discovering reproducible associations.10

Utilizing this bioinformatic-driven design, we selected ~10000 SNPs derived from ~1000 genes on a custom-made platform to genotype a modest sample of 753 subjects corresponding to 251 childhood-onset SLE trios (SLE patient and both parents).11 Family-based transmission disequilibrium test and multi-test correction analysis identified SELP and IRAK1 as novel SLE-associated genes with high degree of significance corrected for multiple tests using the false discovery rate (FDR) less than 0.05. Importantly, the original study had also identified a number of genes that although not significant by the accepted criteria were considered to be noteworthy for further investigation (0.05<FDR<0.8). We present here the results on a group of 10 such genes (109 SNPs), obtained in case–control study with a large number of subjects.



In this study, we explored the SLE association of 10 promising genes, each of which showed an FDR of <0.8 in our earlier transmission disequilibrium test-based study. The candidate genes evaluated in this study are BCL6, CASP10, interleukin (IL)-16, IRF5, KIR2DS4, KLRG1, PRL, PTPN22, protein tyrosine phosphatase receptor type T (PTPRT) and toll-like receptor (TLR)8. This case–control study included an independent childhood-onset cohort of 769 childhood-onset SLE and 5337 cases of adult-onset SLE subjects and 5317 healthy controls, each being composed of four ethnicities, as detailed in Supplementary Table 1.

An important component of our approach was the deliberate recruitment and usage of childhood-onset SLE cases. They present a unique subgroup of patients for genetic study because their earlier disease onset, a more severe disease course, a greater frequency of family history of SLE and a lesser effect of sex hormones in disease development12, 13 imply a higher genetic load or a more penetrant expression of this genetic load. However, because childhood-onset SLE may also show the involvement of different genetic factors relative to adult onset disease, we analyzed childhood-onset and adult-onset groups of SLE patients separately.

To account for any potential confounding substructure or admixture, we performed principal component analyses14 as detailed in Materials and methods. Excluding the outliers identified by principal component analyses resulted in low inflation factors in all ethnicities except Hispanic Americans, with only the latter requiring additional PC correction.

As we are performing tests of multiple related hypotheses, controlling for study-wide significance is an important concern to avoid promulgating false positives due to the multiple testing. A classical correction for multiple testing is the Bonferroni correction (or similar family-wise error-rate corrections). Unfortunately, it is both too strict and inappropriate in studies such as the present one because of the underlying assumption that each test is independent, whereas in actuality a complex and unknown interdependence is present among SNPs in linkage disequilibrium (LD).11, 15 In light of this, we have instead calculated an estimate of the FDR that measures the number of false positives (type I errors) we would have to accept to consider a result a true discovery (reject the null hypothesis), using the Benjamini and Hochberg16 procedure, considering the total number of SNPs tested and the four different ethnic groups (Supplementary Table 1). Combined P-values were calculated from the per-ethnicity P-value using the Fisher's method. Table 1 shows that 28 SNPs from 7 genes out of the 10 tested have significant combined association with SLE in adult- or childhood-onset subgroups after correction for multiple testing. The complete data on all SNPs tested in this study are given in Supplementary Table 2. Importantly, these genes include not only the earlier associated genes, PTPN22 and IRF5, but also several novel genes that have not yet been associated with SLE. We did not find significant association with the SNPs genotyped in KIR2DS4, PRL and BCL6 in either childhood- or adult-onset SLE. With the exception of rs2476601 in PTPN22, none of the SNPs that we found to be significant code for amino-acid changes; only rs11073001 in IL-16 is in an exon, but this variant does not encode for a different amino acid. The most significant SNP found was rs4728142 in IRF5, with a combined P-value in adults on the order of 10−29 and a corresponding FDR on the order of 10−27.

Figure 1 shows the association of SNPs from four novel genes, KLRG1, IL-16, PTPRT and TLR8, with SLE in four ethnic groups (European Americans (EA), African Americans (AA), Asian Americans (AsA) and Hispanic Americans (HA)) in childhood- and adult-onset SLE cases. It is noteworthy that the majority of the significantly associated SNPs show significance in multiple ethnicities both in adult-onset and in childhood-onset SLE. Nevertheless, it is also important to notice cases in which SLE association is strongly ethnicity dependent. For example, the SNPs around exon 1 of TLR8 are not significant in AsA but are significant in HA, both in children and in adults. These graphs also show the distribution of significant SNPs in the genes. For example, the significant SNPs in IL-16 are concentrated around exon 18.

Figure 1.
Figure 1 - Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, please contact help@nature.com or the author

Association of SNPs in KLRG1, IL-16, PTPRT and TLR8 with SLE in four ethnic groups (EA, European Americans; AA, African Americans; AsA, Asian Americans; and HA, Hispanic Americans) in childhood- and adult-onset SLE cases. The position of exons (green rectangles, non-coding regions in lighter green) and introns (connecting lines) are indicated in the bottom plot. The dashed horizontal line corresponds to P=0.05. The exact numbers of subjects studied are detailed in Supplementary Table 2. SLE, systemic lupus erythematosus; SNP, single-nucleotide polymorphism; TLR, toll-like receptor.

Full figure and legend (288K)

Next we performed haplotype analyses in different ethnic groups, children and adults separately (Tables 2, 4, 5 and Supplementary Tables 3–5). Supplementary Table 3 depicts the significant haplotype blocks in KLRG1, which are noticeably different in the various ethnicities. Interestingly, no significant haplotype blocks were found in adult EA. The significant haplotype blocks in IL-16 are limited to childhood-onset HA (Supplementary Table 4). As shown in Supplementary Table 5, the significant haplotype blocks in PTPRT were limited to AsA and a smaller block in childhood-onset HA.

IRF5 has a large number of significant haplotype blocks that are similar in the various ethnicities besides AA (Table 2). Comparing our results with the earlier published data on IRF5 association with SLE, we found that rs729302 SNP was reported to be associated with SLE in an EA population with a P-value of 4 × 10−4 17 or 5.2 × 10−7,18 in Swedish cohort with a P-value of 2.7 × 10−4 (not corrected for multitest)19 and in family trios (uncorrected P-value of 5.0 × 10−4).20 We confirmed these findings on an EA cohort with a multitest-corrected FDR of 3.4 × 10−9 in adults and 1.8 × 10−8 in childhood-onset cases as shown in Table 3. Furthermore, we found a significant association of this rs729302 SNP with SLE in HA adults (Q-value of 8.0 × 10−3 (Table 3)) and combined children (FDR of 1.6 × 10−5 (Table 1)), but not in AA or AsA cohorts in either adult- or childhood-onset SLE (Table 3). The previously reported association of rs4728142 in a Swedish cohort19 and family trios 20 was confirmed by us and extended to all four ethnicities in adult-onset, and to all ethnicities, except AA in childhood-onset disease (Table 3). We have also confirmed the involvement of rs2004640 in EA,17, 18, 19 African Americans,18 Chinese21 and family trios,20 and in both childhood- and adult-onset SLE in each ethnicity, except for childhood-onset HA (Table 3). Association with rs752637 in Europeans was shown by some earlier investigators18, 19 but not by others.17 Our studies found a strong association of this SNP with SLE in EA adults (FDR 1.4 × 10−10), but not as strong in adult HA, AsA or children EA cohorts (Table 3).

We have confirmed the earlier association of rs3807306 with SLE showed in a European cohort 19 and in EA and AA 18 and extended this association to HA and AsA (Table 3). The association of rs3807306 in AA was not significant in our study (Q-value of 0.09), but with an uncorrected P-value of 0.03, it does not contradict the results of an earlier study.18 Also, in agreement with Sigurdsson et al.,19 we did not detect association of rs1874328 with SLE (Supplementary Table 2). Underscoring the ethnic dependence of many SNP associations, rs3807135, found earlier to be SLE associated in a family trio study,20 was found by us to be associated in adults only in EA and HA, but not in AsA or AA, with a very low Q-value of 0.51 for adult-onset AA (Table 3). We have also confirmed SLE-associated haplotype block in the same region as reported earlier17, 18 and extended this block in chromosome region and detected its SLE association with other ethnicities (Table 2).

Table 4 shows significant haplotype blocks in TLR8, which are distributed throughout the gene, though childhood-onset EA has a haplotype with much higher significance located in the 5′-UTR of TLR8. In addition, Figure 2 shows the LD between SNPs that compose the haplotype blocks of TLR8 in adult-onset AA (panel a) and childhood-onset AA (panel b) and in EA (panel c). These panels illustrate the differences in LD structure, which lead to distinct haplotype blocks observed in different ethnicities.

Figure 2.
Figure 2 - Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, please contact help@nature.com or the author

Schematic representation of multiple significant haplotype blocks in TLR8 found in adult-onset AA, childhood-onset AA and childhood-onset EA. a: Adult AA; b: Childhood AA; c: Childhood EA. Blocks connecting SNP pairs are shaded according to the strength of the linkage disequilibrium as measured by D′ from 0 (white) to 1 (red). Haplotype blocks are surrounded by thick black irregular pentagons. The haplotype blocks depicted, which are significant, are given in Table 4. AA, African Americans; EA, European Americans; SNP, single-nucleotide polymorphism; TLR, toll-like receptor.

Full figure and legend (257K)

Although no SNPs found in BCL6 survived the multitest correction, the haplotype analysis indicated that a haplotype block in childhood-onset AsA is significantly associated with the disease (Table 5), underlining the utility of haplotype analysis even in the absence of singly significant SNPs. The LD structure that led to this haplotype block is depicted in Figure 3.

Figure 3.
Figure 3 - Unfortunately we are unable to provide accessible alternative text for this. If you require assistance to access this image, please contact help@nature.com or the author

Schematic representation of three haplotype blocks in BCL6 found in childhood-onset AsA. Blocks connecting SNP pairs are shaded according to the strength of the linkage disequilibrium as measured by D′ from 0 (white) to 1 (red). Haplotype blocks are surrounded by thick black irregular pentagons. The haplotype blocks depicted, which are significant, are given in Table 4. SNP, single-nucleotide polymorphism.

Full figure and legend (112K)



Using a large cohort of adult- or childhood-onset SLE cases in four different ethnicities and comparable numbers of relevant controls, we show in this study that seven genes exhibit a significant (FDR<0.05) association with SLE, both confirming some genes that were earlier found to be associated with SLE (PTPN22 and IRF5) and novel findings (KLRG1, IL-16, PTPRT, TLR8 and CASP10) that were not reported earlier. Furthermore, although none of the SNPs within the BCL6 gene achieved significant association with SLE after multitest correction, a haplotype block within BCL6 shows significant association with the disease as well. These genes are additional to IRAK1 and SELP, which we found to be significantly associated with SLE in our first step study,11 and their follow-up studies are being reported separately.

The presented results show the powerful potential of using a two-step Bayesian approach utilizing up-to-date biotechnology and bioinformatic methods for discovering novel genes. This methodology yielded more novel significant results than the much more expensive GWA approach. Indeed, Iles5 re-examined the results of 54 studies across 22 different relatively common complex diseases, most of which were GWA studies with some GWA follow-up studies. Only 45 disease-associated SNPs found initially in GWA studies could be conclusively ascertained as significant. Furthermore, for several diseases, such as Parkinson's disease,22 bipolar disorder23, 24 and hypertension,23 no new replicable variant has yet been found using GWA (as of February 2008). Compared with approximately two new genes identified per disease using GWA, our Bayesian approach yielded many more novel genes. As a case in point, several of the genes discovered using the Bayesian design were missed by the GWA studies performed to date in SLE.25, 26, 27, 28

GWA studies have been praised because of their unbiased nature, namely they are ‘unbiased by prior assumptions about the DNA alterations responsible’.29 However, it does not make much sense to ignore the whole universe of valuable information collected about the pathogenesis of a common disease that has been studied for decades by excellent investigators. The earlier studies, whereas often not resulting (or not even designed) to identify SLE-associated genes, did provide a well-documented background to reveal SLE-associated physiological pathways. Accordingly, our design took advantage of the vast literature on SLE in humans and in mouse models of lupus. The results obtained show that using prior available information as a primary guide allows one to identify novel SLE-associated genes with high confidence.

In each of these novel genes, there is much biological information to form hypotheses as to their involvement in the genetic predisposition to SLE. Thus, the association of KLRG1 (killer cell lectin-like receptor 1) gene (mapped at 12p12) implicates the involvement of NK cells in the genetic predisposition to SLE. KLRG1 is expressed on NK cells and on subsets of activated T cells. KLRG1-expressing NK cells show decreased proliferative activity.30 SLE patients, including childhood-onset cases, have quantitative and qualitative alterations in NK cells.31, 32, 33 The association of SLE with KLRG1 showed in our studies, coupled to earlier findings, that first-degree relatives of SLE patients 33 and healthy monozygotic co-twins of SLE patients34 display reduced numbers and activity of NK cells, suggesting that this latter phenotype might be involved in disease causation rather than being simply a consequence of the disease process.

More recent work has shown that KLRG1 expression defines a novel and distinctive subset of CD4+ Treg cells that depend on IL-2 and express FoxP3 but are only partially overlapping with the CD4+ and CD25+ Treg subset.35 Interestingly, the cytokine IL-16, shown to be elevated in SLE subjects,36, 37 is a natural ligand of the CD4+ molecule and induces CD4 T-cell anergy.38, 39 IL-16 may also induce or recruit CD4+ FoxP3+ T regs in the tissue.40 Thus, the involvement of IL-16 in the genetic predisposition to SLE as shown here might be in the same pathway as KLRG1.

SLE is characterized by the production of autoantibodies to certain cellular macromolecules, such as the small nuclear ribonucleoprotein particles (snRNPs),41 and by the increased expression of type I interferon (IFNA).42, 43 Conserved RNA sequences within snRNPs can stimulate TLR7 and -8, as well as activate innate immune cells, such as plasmacytoid dendritic cells, which respond by secreting high levels of IFNA. Possibly, SLE patients’ sera containing autoantibodies to snRNPs form immune complexes that are taken up through the Fc receptor gammaRII and efficiently stimulate plasmacytoid dendritic cells to secrete IFNAs. Thus, a prototype autoantigen, the snRNP, can directly stimulate innate immunity, suggesting that autoantibodies against snRNP may initiate the autoimmune response by stimulating TLR7/8.41 IFNA, by inducing genes such as IRF5, can exert major effects on the immune system, including inducing a Th1 response and maintaining T-cell activation, while also lowering the threshold for B-cell activation and promoting B-cell survival and differentiation.44 It is likely that genetic variants that change IRF5 activity could result in a prolonged pro-inflammatory response and/or potentially break immunological tolerance. It is, therefore, possible that the genetic involvement of TLR8 gene may at least partially overlap with the IFNA-induced gene, IRF5, in predisposing to SLE.

Importantly, IRF5 signaling has also been shown to play a role in the regulation of cell cycle and apoptosis,45 raising the possibility that susceptibility variants of IRF5 may affect SLE pathogenesis at the level of the apoptosis pathway as well.44 Indeed, the involvement of defective apoptosis in the predisposition of SLE is well documented.46, 47 The association of CASP10 with SLE shown in this paper may further emphasize the importance of apoptosis pathways in the genetic predisposition to SLE. The CASP10 gene locus at 2q23 is mutated in human autolymphoproliferative syndrome type II.48 Patients with autolymphoproliferative syndrome II exhibit prominent non-malignant lymphadenopathy, hepatosplenomegaly, hyperimmunegammaglobulinemia with multiple autoantibodies, autoimmune hemolytic anemia and lymphocytosis with accumulation of normally rare CD4−/CD8− T cells48 as in the lupus-prone MRL/lpr/lpr mice. Importantly, CASP10 is not only involved in Fas signaling but is also essential for apoptosis signaling through multiple death receptors.49

Although further studies will be necessary to prove the involvement of BCL6 in the pathogenesis of SLE, a significant haplotype block within this gene is an important first step in incriminating this transcriptional repressor. BCL6, a frequently translocated oncogene in diffuse large B-cell lymphoma, has also an important function in regulating the differentiation of B cells, T cells and myeloid cells.50 More specifically, BCL6 is required for germinal center formation and is also a critical inhibitor of Th2 responses and inflammation.51, 52

Protein tyrosine phosphatase receptor type T together with the previously associated PTPN2225, 53, 54, 55 underscore the importance of PTPs in the pathogenesis of SLE. PTPRT has been characterized as a key inhibitor of STAT-3,56 which, in turn, mediates transcriptional activation in response to several cytokines involved in the inflammatory response, such as IL-6.

Interestingly, PTPRT is a genetic locus that was suggested to be associated with rheumatoid arthritis in three independent GWA studies.23, 57, 58 In each of these GWA studies, the SNPs within PTPRT lost their significance after multitest correction, further exemplifying the problematic of GWA studies.

In summary, the extensive involvement of these candidate genes in the regulation of the immune response makes their association with SLE potentially very important and justifies subsequent genetic and functional studies.


Materials and methods

Recruitment and biological sample collection

Subjects were enrolled in the Lupus Genetic Study Groups at USC and OMRF, in the PROFILE Study Group at UAB and from additional collaborators using identical protocols. All patients met the revised 1997 ACR criteria for the classification of SLE.59 Ethnicity was self-reported and verified by parental and grandparental ethnicity, when known. Blood samples were collected from each participant, and genomic DNA was isolated and stored using standard methods. Cases were defined as childhood onset according to the criterion that the diagnosis of SLE was made before the age of 13 years by at least one pediatric rheumatologist participating in the study. All protocols were approved by the Institutional Review Boards at the respective institutions.


Genotyping was performed using Illumina iSelect Infinium II Assays on the BeadStation 500GX system (Illumina, San Diego, CA, USA). For analysis, only genotype data from SNPs with a call frequency greater than 90% in the samples tested and an Illumina GenTrain score greater than 0.7 were used. GenTrain scores measure the reliability of the SNP detection based on the distribution of genotypic classes. The average SNP call rate for all samples was 97.18%. To minimize sample misidentification, data from 91 SNPs that had been genotyped earlier on 42.12% of the samples were used to verify sample identity. In addition, at least one sample genotyped earlier was randomly placed on each Illumina Infinium BeadChip and used to track samples throughout the genotyping process.

Statistical analyses

Testing for association was completed using the freely available R module, snpassoc60 and PLINK.61 For each SNP, missing data proportions for cases and controls, minor allele frequency and exact tests for departures from Hardy–Weinberg expectations were calculated. In addition to allelic test of association, the additive genetic model was used as the primary hypothesis of statistical inference. Haploview version 4.062 and the R module genetics (available from http://cran.r-project.org/web/packages/genetics/index.html) were used to estimate the LD between markers and haplotype structures in different ethnicities.

Combined P-values were calculated from the per-ethnicity P-values using Fisher's method. FDR estimates using Q-values were calculated for different ethnicities using the Q-value package (available from http://cran.r-project.org), which implements the Q-value extension of FDR.63 The FDR for combined results were estimated using Benjamini and Hochberg16 procedure, as the proportion of correctly rejected null hypotheses was possibly overestimated when using the Q-value extension, and this procedure provides a more conservative estimation of FDR (but with less power). The FDR corresponds to the proportion of false positives among the results. Thus, an estimate of FDR less than 0.05 signifies that less than 5% of the results accepted as true are false positives and is taken as a measure of significance.

Stratification analyses

To account for potential confounding substructure or admixture in these samples, principal component analyses were performed14 using a large set of SNPs (18446, which were genotyped on these subjects as part of a larger effort). Four principal components were identified that explained a total of ~60% of the observed genetic variation. These were used to identify individuals who were genetically distant from other samples in the same ethnic subset, and thus capable of introducing admixture bias. A total of 378 controls and 569 adult SLE and 80 childhood-SLE cases were identified in this fashion and removed from further analysis as detailed in Supplementary Table 1. After removing these genetic outliers, duplicates and related samples, 5457 independent SLE cases and 4939 controls remained for analysis. We then performed genomic control analysis to calculate the inflation factor λ using the same set of SNPs. This yielded a λ of 1.13 in European American samples, 1.03 in Hispanic Americans, 1.08 in African Americans and 1.04 in Asian Americans. Only the Hispanic sub-population required a principal component analysis correction to remove the final source of confounding through admixture to obtain the inflation factor given above.


Conflict of interest

The authors declare no conflict of interest.



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This work was supported in part by NIH Grant RO1AR445650 to COJ and ALR, Grant 52104 to COJ and RZ. Work at OMRF was supported by National Institutes of Health (AI063622, RR020143, AR053483, AR049084, AI24717, AR42460, AR048940, AR445650 and AR043274), the Alliance for Lupus Research and the US Department of Veterans Affairs. The work at UAB was supported by NIH grants P01-AR49084 and P60-AR48095, SCB was supported by the Ministry for Health, Welfare and Family Affairs, Republic of Korea grants A010252 and A080588.

Supplementary Information accompanies the paper on Genes and Immunity website (http://www.nature.com/gene)